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DeltaConv

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01225-0_4openalex-snapshot

Local Spectral Graph Convolution for Point Set Feature Learning

inferredcites →https://doi.org/10.1007/978-3-030-01237-3_6openalex-snapshot

SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters

inferredcites →https://doi.org/10.1007/978-3-030-01261-8_4openalex-snapshot

Learning SO(3) Equivariant Representations with Spherical CNNs

inferredcites →https://doi.org/10.1007/978-3-030-58592-1_20openalex-snapshot

A Closer Look at Local Aggregation Operators in Point Cloud Analysis

inferredcites →https://doi.org/10.1007/s10462-023-10502-7openalex-snapshot

Geometric deep learning and equivariant neural networks

inferredcites →https://doi.org/10.1016/j.cag.2020.02.005openalex-snapshot

ConvPoint: Continuous convolutions for point cloud processing

inferredcites →https://doi.org/10.1109/34.56205openalex-snapshot

Scale-space and edge detection using anisotropic diffusion

inferredcites →https://doi.org/10.1109/3dv.2016.18openalex-snapshot

SceneNN: A Scene Meshes Dataset with aNNotations

inferredcites →https://doi.org/10.1109/3dv.2019.00015openalex-snapshot

Effective Rotation-Invariant Point CNN with Spherical Harmonics Kernels

inferredcites →https://doi.org/10.1109/cvpr.2014.491openalex-snapshot

FAUST: Dataset and Evaluation for 3D Mesh Registration

inferredcites →https://doi.org/10.1109/cvpr.2015.7298801openalex-snapshot

3D ShapeNets: A deep representation for volumetric shapes

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.11openalex-snapshot

Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

inferredcites →https://doi.org/10.1109/cvpr.2017.16openalex-snapshot

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2017.261openalex-snapshot

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

inferredcites →https://doi.org/10.1109/cvpr.2018.00097openalex-snapshot

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

inferredcites →https://doi.org/10.1109/cvpr.2018.00109openalex-snapshot

Pointwise Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2018.00478openalex-snapshot

Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling

inferredcites →https://doi.org/10.1109/cvpr.2018.00961openalex-snapshot

3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

inferredcites →https://doi.org/10.1109/cvpr.2019.00109openalex-snapshot

Modeling Local Geometric Structure of 3D Point Clouds Using Geo-CNN

inferredcites →https://doi.org/10.1109/cvpr.2019.00319openalex-snapshot

4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2019.00344openalex-snapshot

Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling

inferredcites →https://doi.org/10.1109/cvpr.2019.00571openalex-snapshot

PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing

inferredcites →https://doi.org/10.1109/cvpr.2019.00910openalex-snapshot

Relation-Shape Convolutional Neural Network for Point Cloud Analysis

inferredcites →https://doi.org/10.1109/cvpr.2019.00985openalex-snapshot

PointConv: Deep Convolutional Networks on 3D Point Clouds

inferredcites →https://doi.org/10.1109/cvpr.2019.00986openalex-snapshot

Octree Guided CNN With Spherical Kernels for 3D Point Clouds

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00047openalex-snapshot

A Hierarchical Graph Network for 3D Object Detection on Point Clouds

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00952openalex-snapshot

Going Deeper With Lean Point Networks

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00319openalex-snapshot

PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

inferredcites →https://doi.org/10.1109/cvpr46437.2021.01152openalex-snapshot

CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation

inferredcites →https://doi.org/10.1109/icassp.2018.8462291openalex-snapshot

A Graph-CNN for 3D Point Cloud Classification

inferredcites →https://doi.org/10.1109/iccv.2019.00072openalex-snapshot

Surface Networks via General Covers

inferredcites →https://doi.org/10.1109/iccv.2019.00167openalex-snapshot

Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data

inferredcites →https://doi.org/10.1109/iccv.2019.00534openalex-snapshot

DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing

inferredcites →https://doi.org/10.1109/iccv.2019.00651openalex-snapshot

KPConv: Flexible and Deformable Convolution for Point Clouds

inferredcites →https://doi.org/10.1109/iccv.2019.00764openalex-snapshot

Dynamic Points Agglomeration for Hierarchical Point Sets Learning

inferredcites →https://doi.org/10.1109/iccv48922.2021.00095openalex-snapshot

Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis

inferredcites →https://doi.org/10.1109/iccv48922.2021.00985openalex-snapshot

Field Convolutions for Surface CNNs

inferredcites →https://doi.org/10.1109/iccv48922.2021.01198openalex-snapshot

Vector Neurons: A General Framework for SO(3)-Equivariant Networks

inferredcites →https://doi.org/10.1109/lra.2018.2850061openalex-snapshot

3DmFV: Three-Dimensional Point Cloud Classification in Real-Time Using Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/msp.2017.2693418openalex-snapshot

Geometric Deep Learning: Going beyond Euclidean data

inferredcites →https://doi.org/10.1109/tmm.2021.3074240openalex-snapshot

Geometric Back-Projection Network for Point Cloud Classification

inferredcites →https://doi.org/10.1109/tpami.2020.3005434openalex-snapshot

Deep Learning for 3D Point Clouds: A Survey

inferredcites →https://doi.org/10.1109/wacv.2018.00218openalex-snapshot

General-Purpose Deep Point Cloud Feature Extractor

inferredcites →https://doi.org/10.1109/wacv48630.2021.00018openalex-snapshot

Continuous Geodesic Convolutions for Learning on 3D Shapes

inferredcites →https://doi.org/10.1109/wacv48630.2021.00386openalex-snapshot

Dense-Resolution Network for Point Cloud Classification and Segmentation

inferredcites →https://doi.org/10.1111/cgf.12942openalex-snapshot

Spectral Processing of Tangential Vector Fields

inferredcites →https://doi.org/10.1111/cgf.13252openalex-snapshot

A Dirac Operator for Extrinsic Shape Analysis

inferredcites →https://doi.org/10.1112/blms/16.3.328openalex-snapshot

SEMI-RIEMANNIAN GEOMETRY With Applications to Relativity

inferredcites →https://doi.org/10.1137/120869730openalex-snapshot

Solving Partial Differential Equations on Point Clouds

inferredcites →https://doi.org/10.1145/1399504.1360696openalex-snapshot

Articulated mesh animation from multi-view silhouettes

inferredcites →https://doi.org/10.1145/2504435.2504442openalex-snapshot

Digital geometry processing with discrete exterior calculus

inferredcites →https://doi.org/10.1145/2516971.2516977openalex-snapshot

Geodesics in heat: A new approach to computing distance based on heat flow

inferredcites →https://doi.org/10.1145/2818143.2818167openalex-snapshot

Vector field processing on triangle meshes

inferredcites →https://doi.org/10.1145/2980179.2980238openalex-snapshot

A scalable active framework for region annotation in 3D shape collections

inferredcites →https://doi.org/10.1145/3072959.3073616openalex-snapshot

Convolutional neural networks on surfaces via seamless toric covers

inferredcites →https://doi.org/10.1145/3197517.3201301openalex-snapshot

Point convolutional neural networks by extension operators

inferredcites →https://doi.org/10.1145/3240508.3240621openalex-snapshot

RGCNN

inferredcites →https://doi.org/10.1145/3272127.3275102openalex-snapshot

Multi-directional geodesic neural networks via equivariant convolution

inferredcites →https://doi.org/10.1145/3272127.3275110openalex-snapshot

Monte Carlo convolution for learning on non-uniformly sampled point clouds

inferredcites →https://doi.org/10.1145/3306346.3322959openalex-snapshot

MeshCNN

inferredcites →https://doi.org/10.1145/3326362openalex-snapshot

Dynamic Graph CNN for Learning on Point Clouds

inferredcites →https://doi.org/10.1145/3343031.3351042openalex-snapshot

SRINet

inferredcites →https://doi.org/10.1145/3414685.3417806openalex-snapshot

MeshWalker

inferredcites →https://doi.org/10.1145/3450626.3459797openalex-snapshot

HodgeNet

inferredcites →https://doi.org/10.1145/3507905openalex-snapshot

DiffusionNet: Discretization Agnostic Learning on Surfaces

inferredcites →https://doi.org/10.1145/3596711.3596797openalex-snapshot

SCAPE: Shape Completion and Animation of People

inferredcites →https://doi.org/10.1609/aaai.v33i01.33013558openalex-snapshot

Hypergraph Neural Networks

inferredcites →https://doi.org/10.1609/aaai.v35i4.16414openalex-snapshot

Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud

inferredcites →https://doi.org/10.17169/refubium-7719openalex-snapshot

Discrete Differential Operators on Polyhedral Surfaces - Convergence and Approximation

inferredcites →https://doi.org/10.3390/s19194188openalex-snapshot

Deep Learning on Point Clouds and Its Application: A Survey

inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-snapshot

Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories

inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot

Exploiting Generative AI to Scale up Intelligent Tutoring Systems

inferredcites →https://doi.org/10.48550/arxiv.1312.6203openalex-snapshot

Spectral Networks and Locally Connected Networks on Graphs

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

inferredcites →https://doi.org/10.48550/arxiv.1512.03012openalex-snapshot

ShapeNet: An Information-Rich 3D Model Repository

inferredcites →https://doi.org/10.48550/arxiv.1802.08219openalex-snapshot

Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

inferredcites →https://doi.org/10.48550/arxiv.1806.02952openalex-snapshot

RGCNN: Regularized Graph CNN for Point Cloud Segmentation

inferredcites →https://doi.org/10.48550/arxiv.1812.06254openalex-snapshot

3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN

inferredcites →https://doi.org/10.48550/arxiv.1902.04615openalex-snapshot

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

inferredcites →https://doi.org/10.48550/arxiv.1904.10014openalex-snapshot

Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features

inferredcites →https://doi.org/10.48550/arxiv.1911.02163openalex-snapshot

SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation

inferredcites →https://doi.org/10.48550/arxiv.2006.04115openalex-snapshot

DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling

inferredcites →https://doi.org/10.48550/arxiv.2006.05353openalex-snapshot

MeshWalker: Deep Mesh Understanding by Random Walks

inferredcites →https://doi.org/10.48550/arxiv.2006.10503openalex-snapshot

SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

inferredcites →https://doi.org/10.48550/arxiv.2010.12455openalex-snapshot

Primal-Dual Mesh Convolutional Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.2104.12826openalex-snapshot

HodgeNet: Learning Spectral Geometry on Triangle Meshes

inferredcites →https://doi.org/10.48550/arxiv.2104.13478openalex-snapshot

A Realization-Theoretic Foundation for Fault Diagnosis, with Diagnosability Equivalence Predictions

inferredcites →https://doi.org/10.48550/arxiv.2106.06020openalex-snapshot

Coordinate Independent Convolutional Networks -- Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4772787openalex-extracted

inferred← citeshttps://doi.org/10.2139/ssrn.4785020openalex-extracted

Improving Point Clouds Classification and Segmentation by Generating Coarse-to-Fine-Grained and Cross-Modal Multi-Scale Features

inferred← citeshttps://doi.org/10.1002/cav.70050openalex-snapshot

Coarse‐To‐Fine 3D Craniofacial Landmark Detection via Heat Kernel Optimization

inferred← citeshttps://doi.org/10.1007/978-3-031-50069-5_8openalex-snapshot

Hierarchical Edge Aware Learning for 3D Point Cloud

inferred← citeshttps://doi.org/10.1007/978-3-031-92369-2_17openalex-snapshot

Neural Descriptors: Self-supervised Learning of Robust Local Surface Descriptors Using Polynomial Patches

inferred← citeshttps://doi.org/10.1007/978-3-032-38236-8_47openalex-snapshot

SurfMark3D: Surface-Based Graph Convolution Framework for Distal Femoral Landmark Localisation in Total Knee Arthroplasty

inferred← citeshttps://doi.org/10.1007/978-981-97-5615-5_17openalex-snapshot

CHMF: Colorful Human Reconstruction Based on Mesh Features

inferred← citeshttps://doi.org/10.1007/s00138-023-01491-2openalex-snapshot

DetailPoint: detailed feature learning on point clouds with attention mechanism

inferred← citeshttps://doi.org/10.1007/s00138-024-01543-1openalex-snapshot

A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

inferred← citeshttps://doi.org/10.1007/s11263-024-02269-3openalex-snapshot

Basis Restricted Elastic Shape Analysis on the Space of Unregistered Surfaces

inferred← citeshttps://doi.org/10.1016/j.cag.2023.03.008openalex-snapshot

Multi Point-Voxel Convolution (MPVConv) for deep learning on point clouds

inferred← citeshttps://doi.org/10.1016/j.cag.2023.06.027openalex-snapshot

Toward Mesh-Invariant 3D Generative Deep Learning with Geometric Measures

inferred← citeshttps://doi.org/10.1016/j.cag.2024.103985openalex-snapshot

Bijective upsampling and learned embedding for point clouds correspondences

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110477openalex-snapshot

Learning hemodynamic scalar fields on coronary artery meshes: A benchmark of geometric deep learning models

inferred← citeshttps://doi.org/10.1016/j.engappai.2026.116218openalex-snapshot

Data-efficient 3D perception of multi-branch wire harnesses via Dual-Domain point cloud segmentation

inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126960openalex-snapshot

Exploring multi-scale and cross-type features in 3D point cloud learning with CCMNET

inferred← citeshttps://doi.org/10.1016/j.gmod.2026.101339openalex-snapshot

NeMoCo: Self-supervised contrastive learning for ultrastructural 3D neuroscience morphologies

inferred← citeshttps://doi.org/10.1016/j.neucom.2025.129593openalex-snapshot

Enhancing U-Net with low-rank attention skip block for 3D point cloud segmentation

inferred← citeshttps://doi.org/10.1016/j.patcog.2025.112132openalex-snapshot

LiGAPU: A LiDAR point cloud upsampling network for multiple complex scenes

inferred← citeshttps://doi.org/10.1016/j.sigpro.2025.109930openalex-snapshot

Topological signal processing and learning: Recent advances and future challenges

inferred← citeshttps://doi.org/10.1016/j.sigpro.2026.110851openalex-snapshot

Selective structural ablation for efficient 3D point cloud signal processing

inferred← citeshttps://doi.org/10.1051/ijmqe/2026019openalex-snapshot

Tunnel lining deformation prediction and construction control based on integrated 3D laser scanning and graph neural network

inferred← citeshttps://doi.org/10.1080/10095020.2025.2588064openalex-snapshot

Improving the segmentation of confusable indoor structures using point convolution and sparse vector attention

inferred← citeshttps://doi.org/10.1088/1361-6501/adc029openalex-snapshot

CA-Net: a context-awareness and cross-channel attention-based network for point cloud understanding

inferred← citeshttps://doi.org/10.1109/3dv62453.2024.00020openalex-snapshot

CloSe: A 3D Clothing Segmentation Dataset and Model

inferred← citeshttps://doi.org/10.1109/ccsb63463.2024.10735524openalex-snapshot

Adaptive Point Cloud Resampling Study Based on Unbalanced Optimal Transport

inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.00133openalex-snapshot

Understanding and Improving Features Learned in Deep Functional Maps

inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.00760openalex-snapshot

Single Mesh Diffusion Models with Field Latents for Texture Generation

inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.02643openalex-snapshot

CurveCloudNet: Processing Point Clouds with 1D Structure

inferred← citeshttps://doi.org/10.1109/icsp62129.2024.10846792openalex-snapshot

Unveiling the Power of Dual Positional Encoding in Point Cloud Learning with DPAtteNet

inferred← citeshttps://doi.org/10.1109/jiot.2025.3546874openalex-snapshot

Toward Visual Interaction: Hand Segmentation by Combining 3-D Graph Deep Learning and Laser Point Cloud for Intelligent Rehabilitation

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Learning Cross-Attention Point Transformer With Global Porous Sampling

inferred← citeshttps://doi.org/10.1109/tmm.2025.3581747openalex-snapshot

PointMT: Efficient Point Cloud Analysis With Hybrid MLP-Transformer Architecture

inferred← citeshttps://doi.org/10.1109/tpami.2024.3445463openalex-snapshot

A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective

inferred← citeshttps://doi.org/10.1109/tvcg.2023.3281781openalex-snapshot

What's the Situation With Intelligent Mesh Generation: A Survey and Perspectives

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Supervertex Sampling Network: A Geodesic Differential SLIC Approach for 3D Mesh

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RIMeshGNN: A Rotation-Invariant Graph Neural Network for Mesh Classification

inferred← citeshttps://doi.org/10.1109/wacv61041.2025.00624openalex-snapshot

3D Understanding of Deformable Linear Objects: Datasets and Transferability Benchmark

inferred← citeshttps://doi.org/10.1111/cgf.15016openalex-snapshot

Surface‐aware Mesh Texture Synthesis with Pre‐trained 2D CNNs

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Tangent Blow‐Ups for Processing Non‐Manifold Geometry

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PoissonNet: A Local-Global Approach for Learning on Surfaces

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Neural Particle Automata: Learning Self-Organizing Particle Dynamics

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Improving Point Clouds Classification and Segmentation by Generating Coarse-to-Fine-Grained and Cross-Type Multi-Scale Features

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Topological Signal Processing and Learning: Recent Advances and Future Challenges

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A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds

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Attentional Keypoint Detection on Point Clouds for 3D Object Part Segmentation

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A-Predator: A Multibeam Echosounder Point Cloud Registration Network with Anisotropic Kernel Point Convolution

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Analysis of large scale 3D shape collection with learning based approaches

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Medical imaging, shapes and statistics for stroke prediction in atrial fibrillation

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